AI Analysis
Final verdict: SAFE
The package exhibits low risks across all assessed categories with no indications of malicious behavior. The metadata risk slightly increases due to limited package history and author activity.
- No network or shell execution risks detected.
- Low metadata risk but notable given the lack of additional context from the author.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- Shell: No shell execution patterns detected, indicating no direct system command execution risk.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
- Metadata: The author has only one package and lacks PyPI classifiers, suggesting low activity or effort.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 4.0
2 maintainer concern(s) found
Author "G Abijith" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with abijith-nlp-v1
Create a social media sentiment analyzer using the 'abijith-nlp-v1' Python package. This application will allow users to input text from various social media posts (e.g., tweets, Instagram captions) and analyze the sentiment behind the text. Additionally, it should identify key entities such as names of people, organizations, locations, etc., which could provide insights into who or what the post is talking about. Step 1: Set up your development environment. Ensure you have Python installed, then install the 'abijith-nlp-v1' package via pip. Step 2: Design a user-friendly interface where users can paste their text. This can be a simple console application or a more sophisticated web-based interface using Flask or Django. Step 3: Implement the sentiment analysis feature using 'abijith-nlp-v1'. This involves calling the appropriate function from the package to process the input text and return a sentiment score indicating whether the text is positive, negative, or neutral. Step 4: Extend the functionality to include entity recognition. Use 'abijith-nlp-v1' to extract named entities from the text and categorize them into types like person, organization, location, etc. Step 5: Display the results in an easily understandable format. For example, show the overall sentiment score, the identified entities with their categories, and perhaps even highlight these entities within the original text. Suggested Features: - Option to analyze multiple texts at once - Visualization of sentiment scores over time if historical data is available - Exporting results to CSV or PDF for further analysis - Error handling for invalid inputs or unexpected issues The 'abijith-nlp-v1' package simplifies the process of performing complex NLP tasks by providing easy-to-use functions. Utilize its sentiment analysis and entity recognition capabilities to enrich the user experience and provide deeper insights into the text data.